# Senior Data Engineer

**Company:** [Polymarket](https://hotfix.jobs/companies/polymarket)
**Location:** New York, NY
**Role:** Data Engineering
**Experience:** 5+ years
**Skills:** ClickHouse, Kafka, Postgres, Iceberg, Parquet, Snowflake, BigQuery, Redshift, Apache Pinot, SQL, Python, Rust
**Posted:** 2026-04-29

> Builds and scales OLAP/OLTP data platforms for prediction market analytics, handling event ingestion, data modeling, streaming pipelines, and low-latency serving at crypto scale. Requires 5+ years experience with columnar warehouses like ClickHouse, Kafka, PostgreSQL, and data lakes.

## Job Description

## What You'll Do

- Own the OLAP analytics layer. Drive our columnar warehouse environment end-to-end: raw event ingestion → cleaned facts and dimensions → business aggregates → API-serving views. Own materialized view design, refresh cadences, dictionary catalog, query planning, and cost optimization.
- Partner on the OLTP serving layer. Work closely with the team on high-write serving tables that back our product APIs – partition strategy, indexing, trigger pipelines, autovacuum tuning, and bloat monitoring – with sub-100ms read-path discipline.
- Shape streaming and data lake infrastructure. Define Kafka topic schema contracts, evolve the S3 lake layout with modern table formats, and contribute to parity-validation tooling that guards data correctness under migration pressure.
- Design data models at scale. Work with event-sourced, append-mostly data with chain-reorg semantics. Design the derivative analytics – PnL, realized/unrealized position tracking, cohort metrics – and formalize ownership boundaries between upstream ingestion and downstream analytics.
- Coordinate across teams. Negotiate schema contracts with the warehouse-owning team and downstream consumers including frontend, notifications, and third-party integrators.

## What We're Looking For

- 5+ years of data engineering on production systems serving real users at scale
- Deep knowledge of OLTP/OLAP split architectures: you know when a row store wins, when a column store wins, and when to use both
- Columnar warehouse expertise: **ClickHouse** strongly preferred; Snowflake, BigQuery, Redshift, or Apache Pinot accepted if fundamentals are solid
- Data lake experience: **Parquet**, **Iceberg** (or Delta/Hudi), compaction strategies, S3 layout discipline
- Streaming pipeline experience: **Kafka**, exactly-once vs. at-least-once reasoning, backpressure, consumer-group patterns, schema evolution
- Strong data modeling fundamentals: star/snowflake, SCD patterns, CDC, idempotent event sourcing, dimensional vs. event-log tradeoffs
- **PostgreSQL** at scale: partitioning, index design, autovacuum/bloat remediation, query planning, CDC triggers vs. logical replication
- SQL fluency at warehouse scale: window functions, CTEs, dictionary-based enrichment, dialect specifics
- Distributed systems reasoning: consistency models, event ordering, replay semantics, write-once vs. mutable state, reorg handling

**(Plus)**
- EVM indexing experience: rindexer, subgraphs, or comparable
- **Rust**: you'll touch indexer and validation tooling codebases; comfortable reading and contributing
- Domain knowledge in DeFi, prediction markets, or order-book systems
- Observability and SLO thinking: Prometheus metrics design, dashboard discipline, alert-fatigue avoidance
- **Python** for SQL tooling, ad-hoc analysis, and one-off migrations
- Track record shipping a platform migration or greenfield data stack under a hard deadline

## Benefits

- Competitive salary & equity
- Unlimited PTO
- Full Health, Vision, & Dental coverage
- 401k match
- Hardware setup: new MacBook Pro, big display, & accessories

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